Automatically Assigning Semantic Role Labels to Parts of Documents

ABSTRACT

Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of PCT Application Serial No.PCT/US20/43606, “Cross-Document Intelligent Authoring and ProcessingAssistant,” filed Jul. 24, 2020. This application also claims priorityunder 35 U.S.C. § 119(e) to U.S. Provisional Patent application Ser. No.62/900,793, “Cross-Document Intelligent Authoring and ProcessingAssistant,” filed Sep. 16, 2019. The subject matter of all of theforegoing is incorporated herein by reference in their entirety.

BACKGROUND 1. Technical Field

This disclosure relates generally to methods and apparatus for the AIself-supervised creation of hierarchically semantically labeleddocuments and/or for the assisted authoring and processing of suchdocuments.

2. Description of Related Art

Many businesses create multiple documents that are quite similar eventhough they are customized each time. For example, an insurance officemay produce many proposals for a particular kind of insurance, but eachmust be tailored to the particular customer's needs. These documents canbe considered to be of the same “type,” because they have similar text(and possibly image) content (reflecting similar purposes and topics),similar selections and arrangements of large units such as sections, andoften even similar geometric layout and formatting characteristics.

Some types of documents are widely known and used, but many are not.Many are specific to a particular business, market, or application, andnew ones are created for new situations. Users who may be called“authors” or “editors” commonly create new documents of a particulartype (sometimes called “target documents”) by copying an earlierdocument of the same type and then making changes as needed, for exampleby manually editing or replacing certain chunks of content.

In current practice, word processing identifies chunks typically only ifneeded to achieve formatting: for example, headings, footnotes, andfigures may be explicitly marked in order to obtain special formatting;but names, addresses, or dates are rarely explicitly marked. Even whenidentified, chunks are commonly associated only with formatting effects(such as margins, fonts, and so on) which are useful information, but donot directly provide any indication of their datatypes or semanticroles. Similarly, word processors often represent hierarchicalcontainment only visually: there is often no explicit representation ofnested sections per se, but only of differently formatted headings.

When creating a new document of the same general kind as priordocuments, in many cases the bulk of the work is text editing,replacement, removal, or insertion of certain chunks, being careful notto confuse ones that have different semantic roles (such as swappingbuyer and seller addresses). This typically requires human interventionbecause authoring systems typically know nothing of these chunks,particularly their datatypes or semantic roles, and therefore cannothelp very effectively.

In some simple cases, “forms” and “templates” may be used, providingexplicit places to fill in content for particular chunks. However, formstypically address only simple cases, where substantially all the neededchunks can be enumerated ahead of time, and where there are few large,repeatable, or highly-structured chunks. Forms also require skilledeffort to create, are difficult to adjust to changing circumstances, anddo not actively assist the writer.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure have other advantages and features whichwill be more readily apparent from the following detailed descriptionand the appended claims, when taken in conjunction with the examples inthe accompanying drawings, in which:

FIG. 1 is a block diagram of one implementation of a system and processfor the creation of hierarchically semantically labeled documents usingmachine learning and artificial intelligence.

FIG. 2 is a screenshot showing a dashboard that tracks the processing ofdifferent document sets through the system of FIG. 1.

FIG. 3 is a screenshot of a user interface for receiving feedback fromthe user.

FIG. 4 is a screenshot of an integration with other softwareapplications.

FIG. 5 is a block diagram of one embodiment of a computer system thatmay be used with the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Overview

A group of documents that are determined to be of the same type,constitutes a “document set” or “document cluster”. For example, aninsurance company's proposals for a certain kind of insurance for acertain class of customers may be considered the same type and form adocument set. The same company's proposals for a different kind ofinsurance, or proposals for customers they consider different, may beconsidered a different type belonging to a different document set.Rental agreements, clinical notes for a certain kind of patient, salesproposals, schedules, meeting minutes, etc. are other potential types ofdocuments, as are sub-types that share distinctive patterns of content,structure, and/or layout.

The creation and editing of new target documents within a document setvery often involves editing or replacing “chunks” that are“semantically-significant”: such a chunk is a particular portion of adocument, typically though not necessarily a contiguous span of text,that has a particular datatype and semantic role, and is of meaning andsignificance for a business or other process.

These chunks are of various datatypes, which are more fine-grained herethan atomic datatypes in many computer systems. For example, a givenchunk may represent not merely a string, but a personal ororganizational name; a date; a duration of time (not at all the samething as a date); a currency amount. Larger chunks can include lists ofdrugs or other substances, itineraries, procedures to follow, bundles ofinformation such as a medical prescription; and countless more.

In addition, chunks may have semantic roles in relation to the documentin which they occur. For example, a personal name could be the “tenant”in a lease agreement, or the “seller” in a sales proposal, or the“agent” of another person. A date could represent the start or end ofsome responsibility or activity. A dollar amount could be a periodicpayment amount, or a penalty or bonus related to certain conditions,etc. Such semantic roles are important to making proper use of theinformation in the chunks. Names for semantic roles are called “semanticrole labels”, or simply “labels”.

A chunk is typically represented as a bundle including its location,datatype, semantic role, and/or other data/metadata. A location iscommonly represented as a starting and ending point, which can berepresented in several ways, such as inserted markers or byte,character, or token offsets (either global to a document, or relative toestablished IDs, markers, or other objects). Semantic roles arerepresented by a label or other identifier. Chunks can be of any size,and some can contain other chunks as “sub-chunks”. Chunks can containnot only text, but also non-text data such as images or other media and“structures” such as tables, lists, sections, etc.

The technologies disclosed herein use machine learning, artificialintelligence, and other computer-implemented methods to identify varioussemantically important chunks in documents, automatically provide themwith appropriate datatypes and semantic roles, and use this enhancedinformation to assist authors and to support downstream processes. Chunklocations, datatypes, and semantic roles can often be automaticallydetermined from what is here called “context”, to wit, the combinationof their formatting, structure, and content; those of adjacent or nearbycontent; overall patterns of occurrence in a document; and similaritiesof all these things across documents (mainly but not exclusively amongdocuments in the same document set). “Nearby content” includes contentwhich is horizontally close, such as preceding and following in thereading sequence of text; but also vertically close, such as within thesame container structures like lists and sections along with theirrespective markers, headings, levels, etc. Similarity is not limited toexact or fuzzy string or property comparisons, but may includesimilarity of natural language grammatical structure, ML (machinelearning) techniques such as measuring similarity of word, chunk, andother embeddings, and the datatypes and semantic roles ofpreviously-identified chunks.

For example, a personal or organizational name commonly can beidentified as having a semantic role such as “seller” because thedocument says so, very often using a sentence(s) in some human language,but often also involving larger context. For another example, one ormore words can often be identified as representing a datatype such as“medication name” easily; but context is required to determine that itcarries the semantic role of an allergy rather than of a prescription.Often important evidence of a semantic role is not in the same sentence,but expressed in various other ways such as by the chunk occurring in alarger chunk (such as a “Known Allergies” section). The flexibility andvariety of grammar and of document structural organization (not tomention typos, transcription errors, etc.) make identifying datatypesless difficult but identifying semantic roles, especially ones withscope greater than a single sentence, very difficult.

A given semantic role may relate a chunk to the document as a whole, orto other chunks. For example, the departure time of a flight is tied toone particular “leg” in an itinerary, and only indirectly related toothers. Commonly, the hierarchical structure of chunks groups such itemstogether appropriately, such as co-location within sections, tableparts, etc.

In more detail, examples of semantically-significant chunks include thenames, addresses, and other characteristics of particular parties to acontract; prescribed medications and proscribed procedures in medicalrecords; requirements (or exclusions) in real estate proposals; datesand flight numbers in itineraries; and so on. These can all beconsidered semantic roles of chunks. There are also larger chunks withvarious types and roles, such as whole sections and subsections. Theseare often inserted or removed as wholes, perhaps also with smaller chunkchanges within. Chunks may be hierarchical; that is, larger “containing”chunks may contain other “sub-chunks” to any number of levels.

A chunk is commonly a contiguous series of words in a document, such as“John Doe”. However, chunks may include partial words. “John Doe'shouse” includes a name, but the name ends before the apostrophe (inmid-word). Chunks can even be discontinuous, for example the same namein “John (also known as ‘Bill’) Doe”. Layout can also cause chunks to bediscontinuous, for example a page break can occur in the middle of achunk (perhaps with page headers, footers, or footnotes, which for somepurposes may be ignored); an intervening figure, table, chart, sidebar,or other display; and so on.

The actual location and context of a chunk may also be important—a chunkis not merely an isolated string, which might occur many times withdifferent (or no) semantic roles for different instances. More modernsystems typically support inline or standoff markup, sometimes called“annotation”, that can persistently associate various labels and otherinformation with chunks. For example, HTML provides tags for manuallylabelling the boundaries of generic structure chunks (“div”, “ol”,etc.), and a few broad types or roles for (typically) smaller chunks(“acronym”, “kbd”, “dfn”, “cite”, etc.). Other XML schemas provide manyother labels, and word processors permit somewhat similar labelling via“styles”.

Some chunks may represent what are commonly called “fields.” These areoften small chunks, and often occur in similar contexts and layouts inmany or all documents in a given set, but usually with different textcontent in each. They may also occur multiple times in a singledocument, with the same or very similar content. Such chunks may becalled “field chunks”. They are often manually discovered and treated as“fields” in template-based systems, but here they are discovered bytheir contexts and patterns of occurrence within and across documents,and are assigned datatypes and semantic roles in much the same way asother chunks. They may or may not represent Named Entities such aspersonal names, addresses, dates, etc.

Another common type of chunk may be called a “structure” or “structural”chunk. Such chunks are typically larger, and often contain many otherchunks (some of which may also be structure chunks). They frequentlyhave a “title” or “heading” that provides a name, number, description,and/or other information about the structure chunk. Examples ofstructure chunks include chapters, sections, tables, figures, sidebars,and many more. The types and semantic roles of structure chunks areoften important for determining the types and semantic roles of nearbyor contained chunks.

Not only datatypes, but also specific semantic roles, are important toproperly composing and utilizing a document: It matters a great dealwhether a certain name represents the buyer versus the seller, or thepatient versus the doctor; whether a given date is the start or end of arequirement, or the departure vs. arrival time of a flight; whether anumber specifies principal, interest, dosage, temperature, penalty, orsomething else. For larger chunks, roles include things like being the“limitations on liability” statement versus the “governing law”specification versus a “definition”, and countless others. Chunks'semantic roles are often specific to particular domains or transactionsand are arguably among the most important features of documents. In manykinds of documents, chunks with particular datatypes and semantic rolesare required or at least very common, and chunks are called“counterparts” when they correspond across documents. Counterpart chunksmay occur in similar orders and patterns, especially for documents bythe same author or organization, and usually of the same document set.Counterpart chunks have the same or very similar roles, and commonlyhave similar context and/or formatting. Thus, the distribution of chunkdatatypes and semantic roles provides valuable information todistinguish types of documents, as well as to help in identifyingcounterpart chunks in other documents.

Many counterpart chunks have similar content, but others do not. Forexample the same party (a semantic role) in different documents isusually a different individual, though appearing in very similarcontexts and patterns of use. This may be especially common for, but isnot exclusive to, “field chunks”.

Once discovered, hierarchical semantic chunks with their datatypes andsemantic roles in business documents may be used in downstream businessprocesses. For example, a back-office database can record a new mortgageproperly if it is given the specific parties' names, specific dates, andnumbers such as the term and interest rate, etc. Particularly for suchuses, semantic roles are extremely important: putting the right datatypeinto the wrong database field (such as swapping seller and buyer namesor addresses) is a big problem, particularly when moving information todownstream databases, processes, or reports.

Some Features and Benefits

The technologies described herein may have various features andbenefits, including any of the following.

Some implementations may provide an easier, more efficient, and moreaccurate way to produce documents with hierarchically organized chunkswith semantic labelling that are useful for business processes. This maybe accomplished using a variety of techniques to identify such chunks ofvarious sizes, discover the datatypes and semantic roles they play inthe document, and learn their patterns of use, characteristic contexts,etc. The learning may come from analysis of the content, structure, andformatting of current and prior documents; feedback from authors andeditors; and comparison of multiple documents, especially ones in thesame document set. With this knowledge, the system can provide valuableassistance to users, for example easier creation of higher-quality newdocuments, and extraction of desired information for downstream usessuch as with other software applications, in back-office databases,derived reports, compliance checking, and so on. Such learning may bedone with unsupervised and self-supervised learning techniques, which donot require large amounts of pre-labelled or pre-analyzed data, butinstead infer patterns from unlabeled or minimally labeled data.

Some implementations may enable computers to assist in the writingprocess, by discovering and using patterns within and across abusiness's documents to help writers avoid many of these errors, andthus reduce the time required to achieve a given level of quality.

Today, typical document systems do not identify chunks, or particularlytheir datatypes or semantic roles. This adds time and expense forauthors and editors, and to import the data from documents into back-enddatabases, dashboards, or other downstream business processes. Forexample, it is common to find and copy data manually (chunk by chunk)from contracts into spreadsheets or data-entry forms.

Some implementations may help label such hierarchical semantic chunksduring the authoring process and represent them explicitly, thus makingthem easy for people and/or computers to extract and saving time andexpense in connecting to other business processes of various kinds.

Current technology typically does not take full advantage ofsimilarities between multiple documents created by the same writer orgroup, and/or of the same type (as indicated here by membership in aparticular document set), to identify chunks more reliably in newdocuments or to flag likely-significant differences for attention.Explicit rules such as requiring a section headed “Severability”, onlycover similarities that analysts readily notice and describe; are staticand often constraining (for example, missing cases with rephrasing orreorganization, or failing to respond to countervailing conditions); andquickly become obsolete. Small companies often lack the necessaryresources to develop more responsive technology, and often have too fewdocuments to justify the expense. On the other hand, smaller companiesoften have a less-diverse range of documents, that are more amenable toautomated analysis such as described herein.

Some implementations may use extracted information about chunks andtheir patterns of content, context, layout, and use across documents, toassist writers in creating new documents. Examples include suggesting atleast: specific content to change, reformat, or move; clauses that aremissing in the new document though commonly present in similar documents(called “missing” or “possibly-omitted” chunks or content); clauses thatare present though commonly absent in similar documents (called“unusual” chunks or content); changes such as swapping the names orroles of different parties in particular places; and so on.

Some implementations may accept and retain user feedback, such as when auser indicates that a chunk has been labelled with an incorrect scope,datatype, or semantic role; is not of interest to them; or failed to belabeled at all. Some implementations may use specific user correctionsto improve machine learning and neural models, as well as rememberingnot to repeat earlier suggestions in cases where the user has rejectedthem (even if the additional learning fails to prevent the particularinstance of a mistake). In particular, some implementations may avoidrequiring large numbers of review steps or corrections in favor offew-shot learning techniques and careful choice of what feedback torequest, in order to minimize the amount of user action required. Somecurrent technology learns very specific things, for example when a usertells a spelling-checker to add a word to its dictionary. However, thisinvolves mere rote lists, not iterative training or fine-tuning ofmodels which are used to determine sophisticated later behavior, andtherefore does not fully exploit capabilities such as described herein.

Some implementations may use small numbers of user corrections to learnand improve their behavior, while avoiding annoying users with repeatedsuggestions when improved but still-imperfect models are re-applied.

Many businesses record specific information obtained from documents, indatabases of various kinds that support their processes. For example, acompany that owns many rental properties typically uses a back-endsystem to help manage not just renters' payments, but also specificinformation that originates in their rental agreements, such as approvedpets, prior damage for which the renter is not responsible, or otherinformation. Car or tool renters, mortgage companies, health careproviders, municipalities, and other organizations use otherinformation. Many goods and services have numerous mix-and-matchoptions, and supervisors review statistics on their acceptance,combinations, pricing, and other factors. Business information systemscommonly provide analysis, check consistency or compliance, derivereports, and/or support other business processes, all of which can befacilitated through use of the chunk information described herein.

Commonly, chunks and the information they provide are scatteredthroughout prose text, extracted manually, and entered manually intospreadsheets, databases, or other systems. Manual work has previouslybeen required because important chunks can be expressed in countlessvarying ways because of the flexibility of the natural human languagesin which agreements, emails, and the like are written, and similarlyvariable layout and representation conventions. Negotiations underlyingsuch documents are often also scattered across multiple kinds ofdocuments, including emails, notes from conversations, slidepresentations, etc. That information may also be useful but is typicallyhandled manually. Some systems may treat such information sources asdocuments, gaining the same benefits already described.

Some implementations may provide means for a computer to start executinga specific document once it has been transformed as described herein tobecome a hierarchically semantically labeled document. By combining thehierarchically labeled structure of the document with tools that providevector-semantic representations of text, certain chunks can beidentified as requiring certain actions. For example, a contract mayspecify money transfers, notifications, or other actions, and conditionsthat enable or trigger them. These can be identified and used to startto execute the contract.

Some implementations may provide easy ways to review and summarizeinformation from document sets in interfaces such as “dashboards”, andto move identified information into a customer's back-end databases orsimilar systems, enabling more efficient and less expensive businessdata flows and enhancing quality assurance, consistency, and reporting.Once chunks have been semantically labeled, it becomes easier togenerate summary reports over sets of documents that contain counterpartchunks. Some implementation may provide very easy ways for users tocreate such reports, merely by clicking on one or more examples ofchunks to be included, which are then located and extracted by role orcontext across all documents in a set. Some implementations may alsoassist the user in finding documents that lack expected counterpartchunks, and either correcting them to include or identify such chunks,or confirming that they correctly do not include them.

In another aspect, the performance for a given group such as a companyor department can be enhanced by incorporating information such as chunksemantic roles, patterns of occurrence, and other characteristics oftheir documents and their users' feedback into the system's learningprocesses, and using the resulting improved models to enhance and/orcheck future documents. However, many customers do not want suchinformation shared with other customers, and many have bindingconfidentiality requirements. On the other hand, general information andlearning derived from public, non-confidential sources can be used andshared freely.

Some implementations may provide the benefits of feedback and learningwhile keeping each customer's data and any model information derivedfrom it, separate and private to each customer, while still sharinggeneral learning that is based on non-confidential, public data. Keepingthose data processes separate ensures that information cannot “leak”from one customer to another, even statistically.

Introduction to an Example Implementation

The following is a description of an example system. See FIG. 1. Thissystem relates generally to methods and apparatus for the AIself-supervised creation of hierarchically semantically labeleddocuments and/or for the assisted authoring and processing of suchdocuments. This includes such processes as composing, structuring,annotating, modifying, reviewing, extracting data from the documents,and/or using such data in downstream business processes. Morespecifically, it focuses on documents that are similar to priordocuments, by using mainly unsupervised and self-supervised machinelearning techniques across sets of documents, including relatively smallsets, to discover a detailed hierarchical structure of documents,composed of many semantically meaningful chunks, associated with theirroles; and on the use of such highly-enhanced documents in businessprocesses.

Operation of this example system uses the following processes, which aredescribed in more detail in the following sections. This is merely anexample. Other implementations may use different combinations of steps,including omitting steps, adding other steps and changing the order ofsome steps. They may also use different implementations of the stepslisted below, including different combinations of techniques describedunder each step. In FIG. 1, the steps are preceded by “S”, so step 1below is labelled “S01” and so on.

-   -   1) Import: Bring groups of users' documents into a data store        110.    -   2) Organize: Divide the documents into document sets by type,        such as rental vs. sale agreements, or medical histories vs.        current clinical notes.    -   3) Visual extraction: Extract a linear text stream(s) from each        document based at least on its content and visual layout,        including limited information about distinct text and other        areas, their beginning and ending locations, formats, and        contents. The extracted data may be organized as “visual lines”        or as “visual blocks” (also called “hyperlines” or “visual”        chunks) such as paragraphs distinguished by geometric layout.    -   4) Structure: Identify headings, list items, and other broad        classes of structure chunks in the documents.    -   5) Re-nesting: Determine the nesting relationships of sections        and lists, and the scope of the text of each.    -   6) Topic Chunking: Analyze the topical content of each document        and produce chunks enclosing areas of similar topic (topic-level        chunks).    -   7) Topic Labeling:        -   i) Use embeddings and clustering to produce candidate            datatype and semantic role labels for each heading in the            corpus.        -   ii) Use key phrase extraction techniques to produce            candidate datatype and semantic role labels for chunks.    -   8) Chunk Labeling: Identify and assign (possibly multiple)        datatype and semantic role candidates to other chunks throughout        the documents using a plurality of methods, for example neural        networks, word and character embeddings, grammatical analysis        and pattern matching, regular expressions, similarity metrics,        and/or other methods. Of particular interest for certain        embodiments are:        -   i) Grammatical parsing and pattern-matching on the resulting            structures        -   ii) Use of question-answering technologies to connect small            chunks with particular semantic roles they play in            documents.        -   iii) Combining XPath tree-matching with word-embedding            technology to match patterns in structure and grammatical            trees, despite possibly extensive differences in phrasing            and word choice.    -   9) Named Entity Recognition (NER): Identify and assign datatypes        to chunk that are detected as Named Entities throughout the        documents.    -   10) Role labeling, Extractive labeling: Assign semantic role        labels to the chunks, such as representing that a name        constitutes the “seller” party to a contract, or that a drug is        mentioned as an allergy vs. as a prescription.    -   11) Anomalies: Identify semantic roles that are usually present        or absent in documents of the document set under consideration,        but not in the current document (or vice versa).    -   12) Arbitration: Adjust and/or choose among alternative scopes,        datatypes, and semantic role labels for chunks, producing        Well-Formed structures readily expressible in formats such as        XML.    -   13) DGML: Create an enhanced version of a document, which        contains explicit identification of chunk locations, datatypes,        and semantic role labels, and possibly also additional        information such as the confidence level of each identified        chunk, the datatype expected in similar chunks (such as date,        date range, personal name, and so on), and so on. The enhanced        version is created using an XML-based markup language referred        to as DGML.    -   14) Feedback: Display the enhanced version to a user(s) and        select chunks (and potential locations for possibly-omitted        chunks) to show the users, collecting the user's choice to        confirm, deny, or make other changes. Users can also choose        their own reading and review order freely. Feedback can also        apply to any other interpretations the system has made, such as        organization of documents into document sets as described in        step (2).        -   i) In the case of possibly-omitted chunks, provide            prioritized examples from other documents, that can be            examined and/or copied into the current document as desired,            and automatically customized by applying target-document            values for smaller, nested chunks.    -   15) Feedback response: Track the user's responses to these        interactions, and use that information to fine-tune the models        120, as well as to prevent repeating the same or similar errors        later.    -   16) Downstream communication, Transmit: Select chunks by type        and/or role, and use them to generate reports over document        sets, and/or export them to downstream systems that add function        such as back-end contract databases, regulatory compliance        checkers, management report generators, and so on.

FIG. 2 is a screenshot showing a dashboard that tracks the processing ofdifferent document sets One through Seven, through the process describedabove. In this dashboard, the process is divided into the followingstages:

-   -   Uploading    -   Preprocessing    -   Review Large Chunks    -   Review Small Chunks    -   Ready to Use        The color coding shows the degree of completion. Green stages        are completed, red stages are in process, and black stages are        not yet started.

Each of the steps listed above is described in more detail below.

Further Description of Example Implementation

The numbering here reflects the general order of analysis for thisparticular example. However, not every step depends on every prior step,and, as a result, many elements can be re-ordered or parallelized inother implementations. Elements can also be shifted or even repeated soas to exchange additional information with other elements, or elementscan be run independently, such as in separate processes or machines.

1) Import

The system accepts typical word-processor documents (such as MS Word)and page-layout documents (such as PDF or .png files). In each case,visually-contiguous regions, such as headings, paragraphs, table cells,table, images, and the like are identified and represented as chunks,using a combination of their relative positions, surrounding whitespace,font and layout characteristics, and so on. These features are partlychosen by designers, and partly learned by image and pattern analysis onlarge number of documents. For incoming document that do not havemachine-readable text content already, OCR is also applied.

Those chunks, along with selected layout information, are presented tolater modules in the system.

2) Organize

Users do not have to organize the documents they check in to the system.The system uses clustering methods operating on text content, layoutinformation, and structural information already detected (such asidentification of some headings) to group documents into “sets” forspecific types of documents, for example rental agreements vs. lease vs.sale. The particular document sets found can be checked with the user,and named either automatically or by the user. Once established, thesedocument sets facilitate later machine learning and reasoning about theformat, content, semantic roles, and differences within them. Forexample the system may discover that almost all documents in a given sethave a particular section with three particular sub-chunks of particularroles and datatypes of personal name, one of which recurs in fivedifferent sections. Such patterns are used to help identify similar (anddissimilar) parts of other documents, to suggest review or changes tothe user, and to provide example text for re-use in other documents inthe same (or possibly different) sets.

The clustering of documents into document sets can use features fromdocument structure (the order and containment relations between chunksof various sizes, datatypes, and roles) and layout, as well as textcontent. Once some chunks and/or roles have been identified in at leastsome documents, that information can also be used to improve clustering,either by completely re-clustering, or by smaller adjustments. Forexample, similar documents might become nearly or even entirelyidentical if one ignores the particular content of chunks with the samerole, such as seller and buyer names, addresses, etc.; or checks thatthe pattern of appearance of different chunks is the same, for examplethat one name (say, the seller's) appears in certain places, whileanother (say, the buyer's) appears in certain other places.

The system maintains both the original organization of uploaded filesinto directories (if any), and its own organization of them into sets.Thus users can view both organizations, and learning algorithms can useboth as information. For example, some users name documents according tovarious conventions, and/or organize documents by customer, kind ofdocument, or other features, which are almost always useful forunderstanding patterns of similarity (such as having common chunklocations and roles) and relationships between documents.

3) Visual Extraction

i) Area Finding

The system uses heuristics and machine learning to identify regions indocuments based on geometric patterns. For example, in many documentsmeaningful chunks have a special layout, such as a signature block,abstract, list of definitions, tables, etc. Such patterns can be learnedautomatically by considering geometric and/or layout features,uniqueness or rarity, and/or correspondence either within the samedocument or across documents, especially within the same document set.

Approaches are chosen depending on the format of the incoming document.For example, word-processor documents generally provide explicitinformation about paragraph boundaries, but PDFs or scanned pagesrequire the system to assemble them from visual lines, or even toanalyze whitespace dimensions to assign characters into visual lines(such as in multi-column documents).

ii) Signature Finding

The system creates signatures (also known as “digests”) for documentparts, and uses these to identify and categorize ““interesting”additional chunks and find their boundaries. Signatures are not justbased on text content, but also on the various aspects of context, andmay ignore the content of smaller contained chunks (for example fieldchunks whose content in counterparts varies).

A signature may use even a chunk's pixel representation. The bitmapimage of the text layout is divided into tiles, preferably of size onthe order of 24 pixels square (adjusting for scan resolution), and thetiles are clustered. Autoencoders and neural network processing ofthese, including their neighbor relationships, reveal similar visualevents such as the boundaries between text and rules, edges and cornersof text blocks, even indentation changes and substantial font/stylechanges. Further neural networks then use this clustering to co-identifysimilar layout objects, which frequently indicate or characterizeimportant chunks.

The approach here may use unsupervised approaches for generatingdocument chunk embeddings based on the pixels as well as the charactersin the document chunk, the size of the chunk, its location in thedocument, etc. (as noted, images can also be chunks). Clustering andcomparison techniques can then be used on these embeddings for manydownstream tasks.

iii) Extraction

This aspect takes a post-layout document (e.g., a PDF or scanned printedpage) and transforms recognized character images (“glyphs”) in thedocument into a text stream that represents the correct document orderof the glyphs (the stream may also contain figure or image objects whenappropriate, and there can be multiple streams, such as footnotes orpage headers, which do not have typical places in the reading order). Insome documents there is incomplete explicit representation of thereading order. A well-known example is that there is typically noindication that multi-column layout is in effect at any given point, andthus the first “line” extends only halfway (or less) across, rather thanall the way. However, there are many additional examples where the orderof text may be complex or non-obvious. For example, some layout programsdraw each character separately, making word boundaries non-obvious.Table cells, side-bars, figures, footnotes, and other displays may nothave an obvious position in the text order. Some text such as that inpage headers and footers (as well as end-of-line hyphens) may notrequire a place in the text order at all. Many formats provide noexplicit indication that something is in such special categories.

The system addresses this task by combining visual information(location, style, etc.) of the glyphs, with a deep neural network thatunderstands characteristics of the written language used in the documentto build the text stream. In addition, it detects many basic textboundaries, such as for line, block, column, image, inline font-changes,and header/footer objects.

iv) Represent

Having extracted a text sequence and some hypothesized structure chunks,the system creates a representation of a document (known in one exampleas “DGML”) that includes those as well as information about visualcharacteristics (fonts, colors, sizes, etc.). The representations ofchunks, including information such as their location, type, and role,are called “annotations”. The combined data can then be used by naturallanguage processing (NLP) and deep neural networks (DNN). Deep neuralnetworks incorporate this visual information to assist in structuringthe document into a hierarchy to represent the document structure,including chunks such as headers/bodies, lists/list items, etc.

Sufficient information can be included so that later aspects canconstruct an editable word-processor document which closely resemblesthe original source. This can be included in DGML or a similarrepresentation along with other structure, content, and chunkinformation. In many cases, portions of the document with distinctformatting and layout are also useful chunks. However, formattingcharacteristics that do not coincide with otherwise-needed chunks (andvice-versa) can still be represented, via a special type of chunk, viastandoff annotation, or via other methods.

4) Structure

The structure pipeline converts a flat text file into a hierarchicalstructure, where sections, subsections, and other parts of the documentform an Ordered Hierarchy of Content-Based Objects, a structure known tothose skilled in the art. This conversion is done using unsupervisedmachine learning techniques. This method has several stages:

i) Hyperlining

This involves segmenting the text into “hyperlines”, which are groupslarger than visual lines, and comprise more meaningful logical (asopposed to visual) units such as paragraphs, headings, or similar. Thisis preferably accomplished using a pre-trained neural network whichconsiders features such as the “word shape” of tokens (especiallyleading and trailing tokens), layout information such as font andspacing characteristics, and similar features. Some hyperlines may alsohave been provided by earlier steps (depending on the input document'sformat).

ii) Document Language Model

This preferably uses a Document Language Model which also includesinformation on text content, formatting, and whatever structure has beendiscovered so far, instead of a Language Model based just on the text.This enables better detection of chunks and their hierarchy (such asheaders/bodies, lists/list items, etc.) due to learning to recognizemeaningful chunks and patterns of their occurrence from formatted pages.

This creates a representation of a document that includes both thetextual content and the visual characteristics (geometry, fonts, colors,sizes, etc.). Deep neural networks and NLP processes then utilize suchinformation in the task of structuring the document into a hierarchy ofchunks with datatypes and semantical role labels, by finding the scopesand/or boundaries of various-sized chunks that represent documentstructure. At this stage, the chunks discovered are mainly headings,sections, lists and items, tables, figures, and other relatively largeunits.

iii) Hyperline Clustering

This uses an autoencoder to cluster the hyperlines across the documentset based on the word-shape structure, assigning each hyperline to acluster of hyperlines that are similar in terms of layout, starting andending content, and other characteristics, with each cluster identifiedby a “cluster ID” (this should not be confused with the creation oridentification of document sets).

iv) Inline Headings

A special case of particular interest is “inline headings”, where theheading of a chunk (which sometimes provides the chunk's semantic role)is not on a separate visual line(s) by itself but is on the same line asthe start of following text. Commonly, inline headings are distinguishedtypographically, such as by boldface, underlining, a different font, afollowing colon, or other effects. Separate heuristic and neuralalgorithms identify these chunks.

v) Few-Shot Structure Learning

In spite of the advanced structuring methods described above, it can beexpected that the structure that is generated has certain imperfectionsor does not meet the user's a priori expectations. Few-shot structurelearning takes care of creating a machine learning model relying onfeedback provided by the user, as described in steps (14)-(15). Thismodel is then used to generate a structure that combines the userfeedback on structure with the one already produced by the system (andperhaps iteratively enhanced by prior feedback).

The main principle applied in this case is derived from machinetranslation (MT) methods where a sequence is converted into another. Inthis case, one sequence describing the hyperlines is converted intoanother that also contains start/end markers that encode the hierarchy.

This process takes place in different phases or steps:

-   -   (a) First, a machine translation model is pretrained using a        publicly available dataset.    -   (b) The “dispatcher” (see Section ‘Feedback Response’ for a        description) filters the user feedback.    -   (c) New structure files are generated from the user feedback and        a fine-tuning machine translation data set generated.    -   (d) The pretrained model is further trained using the few-shot        learning principle.

5) Re-Nesting

This aspect uses a “corpus re-nest” algorithm which, given a flat listof cluster IDs, preferably from the Hyperline clustering step,iteratively creates a nested structure using a pushdown automaton. Bycomparing the signatures of neighboring hyperlines, the system candetermine whether a given heading or list item belongs at a more,equally, or less nested level. This allows reconstructing themultiply-nested hierarchical structure of many documents (such aschapters, sections, subsection, clauses, lists, etc.).

Features considered in the re-nesting include the “shape” (as known inNLP technology) of tokens in the hyperlines, especially considering thefirst and last; the particular class of punctuation mark (if any) endinga preceding line; capitalization; formatting information such as leadingwhitespace, indentation, bold, and underline; the presence and form ofan enumeration string at the start of a line (for example, patterns like“IV(A)(1)” or “iv)”) or a particular bullet or other dingbat character;the value of that enumerator; the presence, levels, and values ofpreceding enumerators of the same kind; and so on.

6) Topic Chunking

This aspect uses lexical statistics and other learning techniques oversuccessive chunks of the document, to find where topics shift. Thisenhances the identification of boundaries for large chunks such asentire sections on given topics, because a section (of whatever level)generally has more uniformity of topic, vocabulary, and style within it,than it does with neighboring sections.

7) Topic Labeling

i) Heading Labeler

For each header in the corpus, as shown in FIG. 1, this step

-   -   Creates a numerical representation known as an “embedding” for        each heading.    -   Clusters the headings based at least on those embeddings.    -   Filters out “bad” clusters based at least on measures such as        density, arity, and level of similarity.    -   Propagates the most common semantic role label in each remaining        cluster, to all headings in the said cluster.

ii) Keyphrase Labeler

For each chunk, this step uses an ensemble of key phrase extractiontechniques (such as Rule-based linguistic techniques, ML, Statistical,Bayesian, and/or others) to produce candidate semantic role labels forthe text.

8) Chunk Labeling

i) Grammar

This aspect of the system begins with linguistic analysis of text, suchas natural language processing tasks including Part of Speech Tagging,Dependency Parsing, Constituent Parsing, and others. This system thenapplies tree-matching mechanisms from another domain, to locategrammatical and other structures within the trees or tree-likestructures discovered via NLP. These include document structuringmethods such as tree grammars and tree pattern matching, as exemplifiedby tools such as XPath, GATE, and others.

Using such patterns to identify grammatical phenomena in sentencesenables the system to extract semantic role labels from the text itself,which are then used to annotate nearby chunks. For example, a searchpattern can be constructed that matches the sentence “The following arethe terms of our agreement” (and other sentences with similargrammatical structure) based on the constituency structure of thesentence; and then to extract the noun phrase (in this example, “theterms”) and use it as a semantic role label for one or more chunks inthe content that follows this sentence and contains such “terms”.

ii) Question-Answering

Question answering techniques, including BERT for Question Answering,are specially tuned to identify semantic role labels for candidatechunks (for example, dates, person names, dollar amounts). Mosttraditional question answering models, in contrast, aim to answerquestions like “What is the Effective Date?”. This system instead trainsmodels to answer questions like “What is Jul. 8, 2018?” and aims topredict “Effective Date” or “effective date of X” where X representsanother chunk (not merely “Date”, which is a datatype rather than asemantic role) in the text.

This system also discovers synthetic questions that, when answered, canpoint to relevant information in text. This provides the ability toautomatically pose questions to be used by question-answering.

iii) XPath-Like Rules Integrated with Embeddings

Here, tools in the domains discussed under “Grammar” are integrated withtools that provide vector-semantic representations of text, such asword2vec, char2vec, and many related methods. This system enablesanalysts to express and query for patterns that include both thestructure information well-handled by XPath and similar tools (which caninclude chunk data expressed in XML- or DOM-compatible forms); and thefuzzy or “semantic” similarity information well-handled by vectormodels.

9) NER (Unlabeled Small Chunk)

Technology can identify some chunks by datatype, such as personal orcorporate names, addresses, and so on (this is known as “Named EntityRecognition” or “NER”). However, NER falls well short of identifying thesemantic roles of those entities in the document. Current technologyalso fails to identify larger chunks such as entire clauses or sections,or groups of chunks that comprise meaningful or useful larger chunks.

This aspect of the system detects interesting small chunks, withoutnecessarily also assigning them roles. Many methods and tools exist foridentifying NERs in text. This system uses multiple methods, examples ofwhich are listed below. These innovations are mainly unsupervised:

i) Established NER Methods

ii) Expected Words

Building a model of “expected words, in context, for normal English”, bytraining a language model of n-grams using extensive generic text suchas Wikipedia. When looking at a particular document, the system providesmeans to identify n-grams that do not fit that generic model, and sotend to be special to the document being processed.

iv) TF-IDF

This is a TF-IDF-based approach (“term frequency vs. inverse documentfrequency”), and is used in conjunction with Label Propagation andContextual Semantic Labeling.

v) Sequence Clustering

Extracts small word or character sequences such as n-grams, and clustersthem using contextual embeddings (for example those of BERT). Theexpected result is that n-grams that share semantic meaning will startclustering together. The cost of combinatorial explosion is addressed byusing heuristics (including on the syntax tree) to filter out somen-grams prior to clustering. A wide variety of clustering algorithms maybe applied. In this example, the hdbscan algorithm achieves effectiveclustering while assigning random noise to a “none” cluster.

vi) Few-Shot NER

The system uses few-shot learning techniques to generalize from a smallnumber of labelled instances (for example, selective user feedback), toa more widely applicable rule or adjustment of learned parameters. Thisgreatly reduces the number of times users must be asked for feedback,and more rapidly improves the system's performance.

10) Extractive Labeling

This aspect of the system finds semantic role labels for small chunks,which appear directly in the sentence(s) surrounding the chunk.Meaningful chunks often have their role specified in some form by thecontext. For example:

Jane Doe (the “Seller”), resides at . . .

Rent of $999 must be paid by the end of each month.

i) Contextual Semantic Labeling (CSL)

This process uses neural networks operating on previously-builtstructures including sentence parses, to learn what parts of the textare likely semantic role labels for various chunks. Many chunks mayalready have such labels, with various sources and confidence levels,but this provides additional evidence for or against those, as well asnew labels. Some of the patterns here involve grammar. For example, in“Doe shall pay a rent of $1000 by the last business day of each month”the head verb makes clear what the role of the currency amount is:namely, it is the amount of rent to be paid. Other patterns are learnedautomatically, by supervised and/or unsupervised methods using featuresof the structure, chunking, labeling, and content available in context.Formatting such as parentheses, table layout, key phrases and words, andother features also provide features for the neural network.

Useful information often resides in a containing chunk such as a sectionor subsection, or its heading. For example, whether a given medicationis relevant as a prescription vs. as an allergy might only be detectableby looking at the heading of a containing section (this is anotherexample of why detecting the correct hierarchical nesting of sections isimportant). Many other clues exist that can be learned by machinelearning techniques and applied to discover the applicable roles forvarious chunks. Cross-document similarities can also be used, especiallywith documents in the same document set, to associate semantic rolesthat were discovered for similar contexts but might not be discoverablefor a document in isolation.

ii) Label Propagation

This process standardizes labels across similar chunks of text in acorpus of documents. It applies both to labels extracted from context,and to labels available from prior steps. The algorithm usesAgglomerative Clustering to cluster chunks based on their embeddings,ranks candidate labels for each cluster of chunks using a weightedPageRank algorithm (which uses the frequency/confidence of the labels asinitial node weights), and uses co-occurrence and embedding similarityto determine how similar labels are to each other. It then assignslabels to the chunk based on their cluster-level score and how similarthe chunk that we're labeling is to the chunk that the label came from(in terms of content, embeddings, structure, datatype, semantic role,and/or context). Agglomerative Clustering and PageRank algorithms areapplied to propagate labels across similar contexts, and to make labelsmore consistent across a set of documents.

11) Anomalies

This aspect of the system examines multiple documents within a documentset, such as produced in step (2), and identifies chunks which occur inthe current document but do not commonly have counterpart chunks inother documents of the same set, or vice-versa. The counterpart chunksneed not have identical content, structure, formatting, context,datatypes, and semantic roles, but may have variations from one documentto another. Nevertheless, they can be recognized as substantiallysimilar in those ways to other identified chunks.

When the new document includes chunks that are typically not present inother documents of the same set, the user may be queried about some orall of them, in order to confirm that they were actually intended. Inthis example system, such querying will be more prevalent when the chunkin question is common to the new document and the one (if any) on whichit was based, but few others.

When the new document lacks counterpart chunks that are typicallypresent in other documents of the same set, or even in particularlyrelevant external sources (for example, a manual of house style, acompliance requirement, etc.), examples of some or all such chunks aresuggested to the user, with the content drawn from other documents. Thesuggestions may be ranked for the user, depending on factors such asfrequency of use, being most typical (a centroid) of the availablealternatives, or having high probability of co-occurrence with otherchunks present in the new document. The chunk suggestions may beautomatically updated, for example to replace names, dates, and othersub-chunks specific to the documents the examples are drawn from, withvalues drawn from the new document.

Furthermore, the choice of chunks to be suggested for addition ordeletion, can usefully depend on the practices of different authors,editors, or other staff. For example, if the current author's documentsfrequently differ from another author's in a particular way, that mayindicate that the difference is a considered choice, not an error. Onthe other hand, if all the authors working under the same supervisor dosomething one way but the current author differs from that, that mayindicate a greater need for review, at least when first noticed.

The modeling of anomalies considers structure and chunk datatypes andsemantic roles as well as context, content, and format. For example,modeling the patterns of what datatypes and semantic roles of chunksoccur inside, adjacent, or otherwise near others. Violations ofwell-established patterns may be classified as anomalies and presentedfor user feedback like any other anomalies.

12) Arbitration

Many prior steps create and/or operate on chunks of the document whichwere defined as (typically but not necessarily contiguous) ranges ofcharacters, tokens, and/or non-text objects within the linearsequence(s) produced in step (3).

The chunks under consideration at any point, can be represented eitherby “inline” meta-information such as markup, or by “standoff”representations that refer to locations in the text by various kinds ofpointers. In this example, standoff representations are used for mostprocessing, but inline representations are used for some purposes suchas communication with external tools, which often prefer that. Theserepresentations and others are functionally interchangeable, and thechoice between them can be governed by concerns of performance,convenience, and so on.

The representation of chunks includes information about what steps orimplementations created them, how certain they are (the “confidencelevel”), and their specific datatypes and/or semantic role labels. Theremay frequently arise redundant, uncertain, conflicting, or partiallyoverlapping chunks, which we refer to here as “non-optimal”. Forexample, two or more different processes may attach semantic role labelsto the same span of text (or almost the same span, for example oneincluding “Dr.” before a name, and one not). Chunks may be nested,sometimes deeply, but may also overlap arbitrarily (that is, where eachof the overlapping chunks contains some content that is also in theother, and some that does not). Throughout the steps above a system maymaintain representations that can represent large numbers ofannotations, including overlapping or co-located ones.

Such non-optimal chunks are usually undesirable, at least when thedocument is presented to a user. In addition, many state-of-the-art NLPtools prefer non-overlapping structures, as do many document tools andmethods familiar to those skilled in the art, such as XML, JSON, SQL,and other representational systems. The more restricted structuresusually preferred, are often termed “hierarchical” or “well-formed”, andavoid partially-overlapping chunks.

This aspect of the system modifies the collection of chunks to bestrictly hierarchical, and to avoid non-optimal chunks. This can beaccomplished in a plurality of ways. First, chunks can be deletedentirely (that is, the chunks per se; the document content which theyidentified is not deleted). Second, chunk scope may be modified (forexample, by including or excluding one or more characters or tokens fromeither end), to prevent overlap with another chunk(s). Third, chunks maybe determined to be redundant, and merged. Fourth, chunks may be foundcontradictory (for example, if one tool thinks “Essex” is a place, andone a person), and a choice made.

This process includes means to rapidly find cases of partial and/orcomplete overlap; to compare chunks by type, role, and confidence; andto resolve non-optimal cases by modifying chunks and their associateddata. Choosing what chunks to modify, merge, or delete considers severalfactors such as confidence levels; a priori probability of a given chunkdatatype, semantic role, and content; hyponymy between semantic rolelabels; conditional probabilities of occurrence in the given context;number, roles, and distribution of other chunks in the current and othersimilar documents; priorities of the process at that time; customerfeedback about similar cases; and/or other methods.

Modifications may change chunk confidence levels as well. For example,several aspects of the system may apply similar or identical semanticrole labels to the same or nearly the same portion of the document. Inthat case, the labels are typically merged, and the resulting chunk isassigned higher confidence than the individual chunks it subsumes. Inother cases, a choice between contradictory chunk assignments is made,but the chosen chunk may end up with reduced confidence to reflect thatthere was some level of counter-evidence.

This process improves the quality and consistency of chunkidentification and labeling, enables the information to interoperatewith a wide range of tools, and enables the result to be analyzed moreeasily and reliably. The operations just described can be applied at anytime(s), not just at the end. For example, if a prior step uses anexternal tool for some subtask, it may request reduction towell-formedness. Removed or modified chunks can instead be “suspended”,which means they no longer affect processing but can be re-introduced ondemand; this enables such use of non-overlap-supporting tools, withouthaving to re-create prior work from scratch afterward, and increasesprocessing flexibility and speed.

In one approach, all overlapping and/or all non-optimal chunks areresolved before generating a document shown to the user, so that theresult can be readily encoded in hierarchical formats such as the XMLformats used by many modern word processors and other tools. However, itis also possible (even in XML) to maintain multiple possibly overlappingalternatives at specific locations for potential later resolution, suchas by user feedback or improved algorithmic learning.

13) DGML (DocuGami Markup Language)

The enhanced version of a document represents document structure,format, content, and identified chunks, and may identify which steps ofthe process identified which chunks, and with what level of confidence.Some embodiments use XML as the syntax of this representation, althougha wide range of representations could contain substantially the sameinformation, such as other XML schemas, JSON, various databases, customtext or binary formats, and so on.

In this step, the document and the information about its found chunksare converted (or “serialized”) into an XML form that can easily bepassed to other processes, most notably the front-end user interfaceused for feedback, editing, and review; and to formats useful for“dashboard” applications that provide overview, statistical, andcompliance information to other users such as group managers,quality-control staff, and so on.

DGML, Docugami Markup Language, is a particular XML schema for this use,which accommodates all of the described information in one package. Mostprior schemas may deal with structure, content, and sometimes layout,but do not annotate “chunks” in the abstract as described here. Manyprior schemas also do not provide a generalized mechanism where chunkscan be automatically detected and represented on the fly, particularlyalong with confidence levels and provenance information.

It is also possible with some word processor and other tools' fileformats, to “tunnel” the same information by representing it in formsthat are transparent to that format. For example, if a tool supportsembedded comments or metadata, “invisible” text, ignorable attributes,or other similar features, the information described herein can beconcealed within them, permitting the resulting document to be used, andpossibly modified, in that tool; and to be returned to the system withthe tunneled information still available.

14) Feedback Mode Front End

The extensive annotation and analysis attached to documents and theirdiscovered chunks by the methods already described make it feasible toguide users through editing samples, templates, or prior documents, toproduce similar but new documents customized for current needs. Forexample, this system typically identifies the parties and propertysubject to a contract; the medications or conditions listed undermedical history, current findings, and other specific sections ofclinical notes; relevant dates; and so on. By also examining otherdocument of the same document set, this system learns which things areuncommon, common, or required, and can therefore make more usefulrecommendations to the user on what to review and/or update. Forexample, an effective date may be present in nearly every contract in adocument set, but its value may be different in each. Similarly, theparties change, but the kinds of parties are much more consistent.

i) Unguided Feedback

In interacting with the user, the system first requests feedbackregarding the chunks found (or possibly not found) in a few documents.The first few documents presented for feedback will be the “clustercentroids” for a document set. The final few will be “outliers” in thedocument set.

ii) Guided Feedback

After this, the system guides the user to provide feedback by showingthem selected portions of the document, and asking about present orpotential labels for them, their extent, and so on:

-   -   a. “Interesting labels” are determined by a PageRank-based        algorithm and a grammatical and structure model. Among those        labels, a set of low confidence instances is chosen for review.    -   b. When there are no more low confidence labels in the current        document, the same process may be repeated for additional        documents. In some embodiments, models are continuously updated        based on the feedback the user is providing. However, the        feedback can instead be accumulated and applied later, in        batches, and/or offline. Adjustments to the models, in turn, can        affect the choice of chunks and labels thereafter presented for        feedback, and may trigger re-analysis of some documents.    -   c. The system solicits feedback on field and structure chunks        using substantially the same mechanism. In one approach, all        chunk-detectors provide estimates of confidence, which can be        used along with other information to select candidates for        feedback.

Feedback may be requested in different passes for smaller vs. largerchunks, field vs. structure chunks, or in other orders. See FIG. 3 foran example user interface for user feedback. It displays some or allchunks, and allows the user to select particular ones to examine, seeingthe assigned type and/or role, and optionally alternatives. The user canmove chunk boundaries, choose or edit labels, and so on. Prefereably,users can also request that a particular change (such as to the label)be applied to all corresponding or same-type chunks.

15) Feedback Response

-   -   i) Fleet querying is a method that allows the system to query        both private and public data based on user feedback, typically        from multiple users. Chosen examples are both semantically and        syntactically similar to previous failure cases, which increases        the value of the feedback.    -   ii) Dispatcher. The dispatcher is a methodology for connecting        user feedback on the combined output of a number of ML models        and non-ML algorithms back to the particular learning models 120        that can learn from the feedback.

The system allows models to improve from user feedback on its output andfrom user feedback on the output of other learning and non-learningmodels. This is accomplished by using feedback as incremental (alsocalled “fine-tuning”) training data for the several numerical and neuralmodels described. After feedback is used to improve the models, not onlyis the particular document re-evaluated, but all documents in the set,or even all documents for the user. Thus, feedback on each document canimprove chunk identification, role assignment, structure discovery, andthus user assistance, for all documents. This retraining is representedby the dotted connector from step (15) to step (3) in FIG. 1.

A document and all associated information contribute to the learning andto analyses of sets of documents (especially but not exclusively withinparticular document sets), and so improve performance on futuredocuments. For example, once a new chunk has been added to one or moredocuments in a set, it is available for use in future documents (orrevisions of older ones), and can be suggested for future documents. Atsome point the absence of a recently-introduced chunk role, or thepresence of a chunk role less-used recently, may become an anomaly. Thispoint can be chosen by the user at their initiative or in response to afeedback question, or automatically based on the usage curve ofcounterpart chunks over time. For example, if few to no documents in oneset that were composed before a certain time had a chunk of a given roleand/or context (say, an “Exclusions” section), but most or all that werecomposed later do have it, then lack of a counterpart chunk is likelyanomalous in new documents, and may usefully be suggested to the user assuch.

16) Downstream Communication

Having annotated a document(s) with chunk information such as has beendescribed, selected information is converted to particular formatsrequired by external business information systems such as databases,analytics tools, and so on, and passed to those systems, either directlyor through automated and/or manual review steps. For example, names andaddress of particular parties can be copied to the correct fields in adatabase, which could not be done automatically if they were onlyidentified as “names” and “addresses” per se. See FIG. 4 for an exampleof integration with a downstream software application. In this example,chunks have been extracted that represent terms to which a party isexpected to assent, and they are passed to a downstream applicationsimilar to Docusign to be filled out and signed.

FIG. 5 is a block diagram of one embodiment of a computer system 510that may be used with the present invention. The steps described abovemay be implemented by software executing on such a computer system. Thecomputer system 510 typically includes at least one computer orprocessor 514 which communicates with peripheral devices via bussubsystem 512. Typically, the computer can include, or the processor canbe, any of a microprocessor, graphics processing unit, or digital signalprocessor, and their electronic processing equivalents, such as anApplication Specific Integrated Circuit (ASIC) or Field ProgrammableGate Array (FPGA). These peripheral devices may include a storagesubsystem 524, comprising a memory subsystem 526 and a file storagesubsystem 528, user interface input devices 522, user interface outputdevices 520, and a network interface subsystem 516. The input and outputdevices allow user interaction with computer system 510.

The computer system may be a server computer, a client computer, aworkstation, a mainframe, a personal computer (PC), a tablet PC, arack-mounted “blade” or any data processing machine capable of executinginstructions (sequential or otherwise) that specify actions to be takenby that machine.

The computer system typically includes an operating system, such asMicrosoft's Windows, Sun Microsystems's Solaris, Apple Computer's MacOs,Linux or Unix. The computer system also typically can include a BasicInput/Output System (BIOS) and processor firmware. The operating system,BIOS and firmware are used by the processor to control subsystems andinterfaces connected to the processor. Typical processors compatiblewith these operating systems include the Pentium and Itanium from Intel,the Opteron and Athlon from Advanced Micro Devices, and the ARMprocessor from ARM Holdings.

Innovations, embodiments and/or examples of the claimed inventions areneither limited to conventional computer applications nor theprogrammable apparatus that run them. For example, the innovations,embodiments and/or examples of what is claimed can include an opticalcomputer, quantum computer, analog computer, or the like. The computersystem may be a multi-processor or multi-core system and may use or beimplemented in a distributed or remote system. The term “processor” hereis used in the broadest sense to include a singular processor andmulti-core or multi-processor arrays, including graphic processingunits, digital signal processors, digital processors and combinations ofthese devices. Further, while only a single computer system or a singlemachine may be illustrated, the use of a singular form of such termsshall also signify any collection of computer systems or machines thatindividually or jointly execute instructions to perform any one or moreof the operations discussed herein. Due to the ever-changing nature ofcomputers and networks, the description of computer system 510 depictedin FIG. 5 is intended only as one example for purposes of illustratingthe preferred embodiments. Many other configurations of computer system510 are possible having more or less components than the computer systemdepicted in FIG. 5.

Network interface subsystem 516 provides an interface to outsidenetworks, including an interface to communication network 518, and iscoupled via communication network 518 to corresponding interface devicesin other computer systems or machines. Communication network 518 maycomprise many interconnected computer systems, machines andcommunication links. These communication links may be wireline links,optical links, wireless links, or any other devices for communication ofinformation. Communication network 518 can be any suitable computernetwork, for example a wide area network such as the Internet, and/or alocal area network such as Ethernet. The communication network can bewired and/or wireless, and the communication network can use encryptionand decryption methods, such as is available with a virtual privatenetwork. The communication network uses one or more communicationsinterfaces, which can receive data from, and transmit data to, othersystems. Embodiments of communications interfaces typically include anEthernet card, a modem (e.g., telephone, satellite, cable, or ISDN),(asynchronous) digital subscriber line (DSL) unit, Firewire interface,USB interface, and the like. One or more communications protocols can beused, such as HTTP, TCP/IP, RTP/RTSP, IPX and/or UDP.

User interface input devices 522 may include an alphanumeric keyboard, akeypad, pointing devices such as a mouse, trackball, touchpad, stylus,or graphics tablet, a scanner, a touchscreen incorporated into thedisplay, audio input devices such as voice recognition systems ormicrophones, eye-gaze recognition, brainwave pattern recognition, andother types of input devices. Such devices can be connected by wire orwirelessly to a computer system. In general, use of the term “inputdevice” is intended to include all possible types of devices and ways toinput information into computer system 510 or onto communication network518. User interface input devices typically allow a user to selectobjects, icons, text and the like that appear on some types of userinterface output devices, for example, a display subsystem.

User interface output devices 520 may include a display subsystem, aprinter, or non-visual displays such as audio output devices. Thedisplay subsystem may include a flat-panel device such as a liquidcrystal display (LCD), a projection device, or some other device forcreating a visible image such as a virtual reality system. The displaysubsystem may also provide non-visual display such as via audio outputor tactile output (e.g., vibrations) devices. In general, use of theterm “output device” is intended to include all possible types ofdevices and ways to output information from computer system 510 to theuser or to another machine or computer system.

Memory subsystem 526 typically includes a number of memories including amain random-access memory (RAM) 530 (or other volatile storage device)for storage of instructions and data during program execution and a readonly memory (ROM) 532 in which fixed instructions are stored. Filestorage subsystem 528 provides persistent storage for program and datafiles, and may include a hard disk drive, a floppy disk drive along withassociated removable media, a CD-ROM drive, an optical drive, a flashmemory, or removable media cartridges. The databases and modulesimplementing the functionality of certain embodiments may be stored byfile storage subsystem 528.

Bus subsystem 512 provides a device for letting the various componentsand subsystems of computer system 510 communicate with each other asintended. Although bus subsystem 512 is shown schematically as a singlebus, alternative embodiments of the bus subsystem may use multiplebusses. For example, RAM-based main memory can communicate directly withfile storage systems using Direct Memory Access (DMA) systems.

Although the detailed description contains many specifics, these shouldnot be construed as limiting the scope of the invention but merely asillustrating different examples. It should be appreciated that the scopeof the disclosure includes other embodiments not discussed in detailabove. Various other modifications, changes and variations which will beapparent to those skilled in the art may be made in the arrangement,operation and details of the method and apparatus disclosed hereinwithout departing from the spirit and scope as defined in the appendedclaims. Therefore, the scope of the invention should be determined bythe appended claims and their legal equivalents.

What is claimed is:
 1. A method implemented on a computer systemexecuting instructions for analyzing and improving documents, the methodcomprising: accessing a document set that contains a plurality ofdocuments, wherein the document set also identifies chunks within theindividual documents of the document set; automatically assigningsemantic role labels to a plurality of the chunks, wherein the semanticrole labels are descriptive of the semantic roles played by the chunks;and automatically assigning semantic role labels to the chunks (a)comprises using machine learning and/or natural language processingmethods to determine semantic roles for chunks; and (b) is also based onchunks in different documents that are identified as playing the samesemantic role within their respective documents; and using the chunksand their semantic role labels in further processing of documents in thedocument set.
 2. The computer-implemented method of claim 1, wherein theplurality of documents in the document set are all a same document type.3. The computer-implemented method of claim 1, wherein the chunks in thedocument set comprise: field chunks that contain content within thedocuments suitable for use as fields in document templates, wherein someof the field chunks are hierarchical and contain other chunks assub-chunks; and structural chunks that contain content comprisingstructures within the layout of the documents
 4. Thecomputer-implemented method of claim 1, wherein the document setcontains legal documents; and the semantic roles comprise (a) rolesplayed by parties to the legal documents, and (b) roles played by dates,time periods or other expressions of time.
 5. The computer-implementedmethod of claim 1, wherein automatically assigning semantic role labelsto chunks comprises: automatically extracting some of the semantic rolelabels from chunks; and assigning the extracted semantic role labels tochunks.
 6. The computer-implemented method of claim 1, whereinautomatically assigning semantic role labels to chunks comprises: usingmachine learning to automatically extract semantic role labels fromchunks (a) based on the content, layout and contexts of chunks inindividual documents; (b) based on patterns of content, layout andcontexts of chunks across the documents in the document set; and (c)based on datatypes of chunks; and assigning the extracted semantic rolelabels to chunks.
 7. The computer-implemented method of claim 1, whereinautomatically assigning semantic role labels to chunks comprises: usingauto-encoder machine learning techniques to automatically extract someof the semantic role labels; and assigning the extracted semantic rolelabels to chunks.
 8. The computer-implemented method of claim 1, whereinautomatically assigning semantic role labels to chunks comprises:automatically extracting candidate semantic role labels from the chunks;using machine learning to refine the candidate semantic role labels; andassigning the extracted semantic role labels to chunks.
 9. Thecomputer-implemented method of claim 1, wherein automatically assigningsemantic role labels to chunks comprises: automatically extracting someof the semantic role labels from chunks based on similarity of content,layout and/or context of chunks from different documents in the documentset; and assigning the extracted semantic role labels to chunks.
 10. Thecomputer-implemented method of claim 1, wherein automatically assigningsemantic role labels to chunks comprises: assigning candidate semanticrole labels to chunks; grouping chunks into clusters based on similarityof the semantic roles played by the chunks; standardizing the candidatesemantic role labels among the chunks in clusters; and assigning thestandardized semantic role labels to chunks.
 11. Thecomputer-implemented method of claim 1, wherein automatically assigningsemantic role labels to chunks comprises: assigning candidate semanticrole labels to chunks; grouping chunks into chunk clusters based onsimilarity of size and text embedding of the chunks; grouping candidatesemantic role labels into label clusters based on similarity of textembedding of the candidate semantic role labels; standardizing thecandidate semantic role labels based on the chunk clusters and the labelclusters; and assigning the standardized semantic role labels to chunks.12. The computer-implemented method of claim 1, wherein automaticallyassigning semantic role labels to chunks comprises: assigning candidatesemantic role labels to chunks that comprise sections of documents,wherein the candidate semantic role labels are based on headings of thesections; grouping the chunks into clusters based on similarity of thecontent in the sections; standardizing the candidate semantic rolelabels by selecting the most common candidate semantic role label as thesemantic role label for all chunks in a cluster; and assigning thestandardized semantic role labels to chunks.
 13. Thecomputer-implemented method of claim 1, wherein the semantic role labelsare chosen from a predetermined set of semantic role labels.
 14. Thecomputer-implemented method of claim 1, wherein the semantic role labelscomprise labels recognized by a software application used for thefurther processing of documents in the document set.
 15. Thecomputer-implemented method of claim 1, wherein automatically assigningsemantic role labels to chunks comprises at least one of: (a) usingmachine learning to determine semantic roles for chunks based on otherchunks that are nearby or based on containing chunks that contain saidchunks, or (b) using natural language processing methods based ongrammatical structures of nearby chunks to determine semantic roles forchunks.
 16. The computer-implemented method of claim 1, wherein some ofthe chunks are Named Entity References, such chunks are labeled withsemantic role labels for the semantic roles played by the those chunksin the documents, and such chunks are also labeled with a datatype ofthe chunk.
 17. The computer-implemented method of claim 1, wherein someof the chunks are multi-paragraph structures in the documents, and suchchunks are labeled with semantic role labels for the semantic rolesplayed by those chunks in the documents.
 18. The computer-implementedmethod of claim 1, further comprising: estimating a confidence level forthe automatically assigned semantic role labels; based on the estimatedconfidence level, presenting some assignments to a user forconfirmation; receiving user feedback for the automatically assignedsemantic role labels; and improving the machine learning and/or naturallanguage processing methods in response to the user feedback.
 19. Anon-transitory computer-readable storage medium storing executablecomputer program instructions for analyzing and improving documents, theinstructions executable by a computer system and causing the computersystem to perform a method comprising: accessing a document set thatcontains a plurality of documents, wherein the document set alsoidentifies chunks within the individual documents of the document set;automatically assigning semantic role labels to a plurality of thechunks, wherein the semantic role labels are descriptive of the semanticroles played by the chunks; and automatically assigning semantic rolelabels to the chunks (a) comprises using machine learning and/or naturallanguage processing methods to determine semantic roles for chunks; and(b) is also based on chunks in different documents that are identifiedas playing the same semantic role within their respective documents; andmaking the chunks and their semantic role labels available for furtherprocessing of documents in the document set.
 20. A computer system foranalyzing and improving documents, the computer system comprising: astorage medium for receiving and storing a document set that contains aplurality of documents, wherein the document set also identifies chunkswithin the individual documents of the document set; and a processorsystem having access to the storage medium and executing an applicationprogram for analyzing and improving documents, wherein the processorsystem executing the application program: automatically assigns semanticrole labels to a plurality of the chunks, wherein the semantic rolelabels are descriptive of the semantic roles played by the chunks; andautomatically assigning semantic role labels to the chunks (a) comprisesusing machine learning and/or natural language processing methods todetermine semantic roles for chunks; and (b) is also based on chunks indifferent documents that are identified as playing the same semanticrole within their respective documents; and makes the chunks and theirsemantic role labels available for further processing of documents inthe document set